ABSTRACT
Bayesian spatial health modeling, sometimes also known as Bayesian disease mapping, has matured to the extent that a range of computational tools exist to enhance end user's ability to analyze and interpret the variations in disease risk commonly found in human and animal populations. This has been enhanced by the easy availability of geographical information systems (GIS) such as ArcGIS or Quantum GIS. With the addition of a range of packages that can fit Bayesian Hierarchical models (BHMs) it is now possible to carry out both exploration and analysis of spatial health data in one environment. For the application of BHMs to disease mapping, there is an extensive literature now available. Often, this area is termed Bayesian Disease Mapping. BHM often requires sophisticated numerical methods to be employed to provide estimates of relevant parameters. BHM often requires sophisticated numerical methods to be employed to provide estimates of relevant parameters.
